Universität zu Köln

Moellenhoff, Kathrin, Loingeville, Florence ORCID: 0000-0002-4278-2974, Bertrand, Julie ORCID: 0000-0002-6568-1041, Nguyen, Thu Thuy, Sharan, Satish, Zhao, Liang, Fang, Lanyan, Sun, Guoying, Grosser, Stella, Mentre, France ORCID: 0000-0002-7045-1275 and Dette, Holger (2022). Efficient model-based bioequivalence testing. Biostatistics, 23 (1). S. 314 - 328. OXFORD: OXFORD UNIV PRESS. ISSN 1468-4357

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Abstract

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and Cmax are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the 90% confidence interval for these ratios falls between 0.8 and 1.25. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of AUC and Cmax using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of 0.05, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Moellenhoff, Kathrin UNSPECIFIED UNSPECIFIED UNSPECIFIED Loingeville, Florence UNSPECIFIED orcid.org/0000-0002-4278-2974 UNSPECIFIED Bertrand, Julie UNSPECIFIED orcid.org/0000-0002-6568-1041 UNSPECIFIED Nguyen, Thu Thuy UNSPECIFIED UNSPECIFIED UNSPECIFIED Sharan, Satish UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhao, Liang UNSPECIFIED UNSPECIFIED UNSPECIFIED Fang, Lanyan UNSPECIFIED UNSPECIFIED UNSPECIFIED Sun, Guoying UNSPECIFIED UNSPECIFIED UNSPECIFIED Grosser, Stella UNSPECIFIED UNSPECIFIED UNSPECIFIED Mentre, France UNSPECIFIED orcid.org/0000-0002-7045-1275 UNSPECIFIED Dette, Holger UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-686477
DOI:	10.1093/biostatistics/kxaa026
Journal or Publication Title:	Biostatistics
Volume:	23
Number:	1
Page Range:	S. 314 - 328
Date:	2022
Publisher:	OXFORD UNIV PRESS
Place of Publication:	OXFORD
ISSN:	1468-4357
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language MIXED-EFFECTS MODELS; EQUIVALENCE; SIMILARITY; TRIALS; POWER Multiple languages Mathematical & Computational Biology; Statistics & Probability Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68647